Explaining the Reputational Risks of AI-Mediated Communication: Messages labeled as AI-assisted are viewed as less diagnostic of the sender's moral character
arXiv:2509.09645v2 Announce Type: replace Abstract: When someone sends us a thoughtful message, we naturally form judgments about their character. But what happens when that message carries a label indicating it was written with the help of AI? This paper investigates how the appearance of AI assistance affects our perceptions of message senders. Adding nuance to previous research, through two studies (N=399) featuring vignette scenarios, we find that AI-assistance labels don't necessarily make people view senders negatively. Rather, they dampen the strength of character signals in communication. We show that when someone sends a warmth-signalling message (like thanking or apologizing) without AI help, people more strongly categorize the sender as warm. At the same time, when someone sends
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Abstract:When someone sends us a thoughtful message, we naturally form judgments about their character. But what happens when that message carries a label indicating it was written with the help of AI? This paper investigates how the appearance of AI assistance affects our perceptions of message senders. Adding nuance to previous research, through two studies (N=399) featuring vignette scenarios, we find that AI-assistance labels don't necessarily make people view senders negatively. Rather, they dampen the strength of character signals in communication. We show that when someone sends a warmth-signalling message (like thanking or apologizing) without AI help, people more strongly categorize the sender as warm. At the same time, when someone sends a coldness-signalling message (like bragging or blaming) without assistance, people more confidently categorize them as cold. Interestingly, AI labels weaken both these associations: An AI-assisted apology makes the sender appear less warm than if they had written it themselves, and an AI-assisted blame makes the sender appear less cold than if they had composed it independently. This supports our signal diagnosticity explanation: messages labeled as AI-assisted are viewed as less diagnostic than messages which seem unassisted. We discuss how our findings shed light on the causal origins of previously reported observations in AI-Mediated Communication.
Comments: Proceedings of the Eighth AAAI/ACM Conference on AI, Ethics, and Society (AIES 2025)
Subjects:
Human-Computer Interaction (cs.HC); Computers and Society (cs.CY); Emerging Technologies (cs.ET)
Cite as: arXiv:2509.09645 [cs.HC]
(or arXiv:2509.09645v2 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2509.09645
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1609/aies.v8i2.36641
DOI(s) linking to related resources
Submission history
From: Pranav Khadpe [view email] [v1] Thu, 11 Sep 2025 17:35:32 UTC (489 KB) [v2] Tue, 31 Mar 2026 20:10:03 UTC (619 KB)
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